FDO Drives Innovation in Grants Prospect Research Using AI

AI and machine learning are driving change every day to improve communities and solve crucial problems in sectors such as education and public health. AI powers everyday services like Facebook newsfeed or Netflix recommendations to personalize your experience. The ability to leverage data for insights is a prerequisite for keeping pace with rapid changes in the philanthropic sector so that nonprofits and donors can stay abreast and be better prepared to respond — and machine learning is just one way to achieve this.

AI and machine learning have been at the core of Foundation Directory Online (FDO) for the last several years. Foundation Center’s data scientist, David Hollander, took us through the implementation of AI and machine learning to make our fundraising research tool possible, and starter tips for nonprofits interested in the potential of AI and machine learning for their organization.

Making FDO Possible

Machine learning powers the grantmaker, grant and recipient results you achieve through FDO every day. Machine learning has accelerated the process of coding grants based on Foundation Center’s expert Philanthropy Classification System. The algorithms in FDO are constantly trained and improved to achieve better results. This is how FDO is able to match user queries to their most likely funders. It also enables FDO to gather deeper insights and provide grant data analytics for prospect research. FDO handles vast amounts of data that is parsed and analyzed using AI and machine learning. Powered by our data team’s expertise and supported with machine learning, we are now able to deliver more grant data and insights than ever before.

Every year Foundation Center codes a record number of grants for FDO facilitated by AI and machine learning. The more data that is fed into FDO, the more the algorithm trains and improves – and with a record number of grants being coded, the faster FDO learns and improves, empowering FDO users with the most comprehensive data and insights needed to find funding.

Interested in AI and Machine Learning for Your Organization?

If your organization is new to AI and machine learning, there are several considerations to make before embarking on implementation, including figuring out your data storage, cleaning your data and data integrations. David suggests to first: identify the problem you are trying to solve with machine learning, and second: identify the data you need to solve it. While the applications for machine learning are endless, there are also inherent challenges to overcome. The quality of your data and data bias, how heavily certain data is weighted, influence the algorithm’s results. Algorithm bias can be minimized with better sampling of data and balancing the weight given to specific data. These are just some of the areas to take into consideration. With your organization’s objectives in hand and procuring the relevant, quality data, your data scientists can understand which machine learning models your organization’s data will need to achieve your goals.